System and methods for detecting malfunctioning nozzles in a digital printing press

ABSTRACT

A method identifies at least one malfunctioning nozzle in a digital printing press, the digital printing press including a plurality of nozzles. The method includes printing a design on a substrate, acquiring at least one image of the printed design and identifying at least one artifact in the acquired image. The method further includes identifying the malfunctioning nozzle and classifying the at least one malfunctioning nozzle according to the at least one of the acquired image of the printed design, at least a portion of a nozzle pattern and at least a portion of a uniformity pattern.

FIELD OF THE DISCLOSED TECHNIQUE

The disclosed technique relates to printing presses in general, and tomethods and system for detecting malfunctioning nozzles in a digitalprinting press, in particular.

BACKGROUND OF THE DISCLOSED TECHNIQUE

Digital printing presses in general and ink jet based printing pressesin particular (e.g., printing sheets or of labels) are required to printa print job continuously and with minimum waste. Waste is defined asprinted material which is not sellable, substrate which is used to printthe printed design (i.e., the product) and thus does not generaterevenue generation and the like. Ink jet nozzles have some probabilityof malfunctioning. The results of such a malfunction may vary. In somecases the results of such a malfunction may be substantial andnoticeable in the printed image (i.e., and thus affect the sellabilityof the product), while in other cases the result in such a malfunctionmay not be noticeable (i.e., and thus not affect the sellability of theproduct). A digital printing press can attempt to rectify suchmalfunctioning nozzles when information relating to which nozzle ornozzles (e.g., nozzle number and color) malfunctioned is available.

U.S. Pat. No. 6,637,853 to Jude Ahne et al directs to a system fordetecting faulty nozzles in an ink jet printer which includes having aplurality of ink jet nozzles. The system includes a host computer and anink jet printer and an optical sensor. The host computer generates atest pattern that is printed on a print medium. The test patternconsists of multiple test images printed in a vertical stack relative toa reference position. A start bar is printed at the reference position.Each of the test images is printed by a separate nozzle on a print headof the printer, such that there is a test image corresponding to eachnozzle. For a print head having several hundred nozzles, more than onepage of the print medium will be required to complete the pattern. Eachpage on which the pattern is printed includes a start bar at the top. Ifa nozzle malfunctions, there will be no test image printed correspondingto that nozzle, resulting in an empty location. The optical sensor isused to inspect the test pattern to detect any empty locations. Theposition of an empty location correlates to the faulty nozzle thatshould have printed a test image in the empty location. The hostcomputer uses this information to modify the print data that is sent tothe printer in the future.

SUMMARY OF THE PRESENT DISCLOSED TECHNIQUE

It is an object of the disclosed technique to provide a novel method andsystem for identifying malfunctioning nozzles in a digital printingpress. In accordance with the disclosed technique, there is thusprovided a method of identifying at least one malfunctioning nozzle in adigital printing press, the digital printing press including a pluralityof nozzles. The method includes the procedures of printing a design on asubstrate, acquiring at least one image of the printed design andidentifying at least one artifact in the acquired image. The methodfurther includes the procedure of identifying the malfunctioning nozzleand classifying the at least one malfunction nozzle according to the atleast one of the acquired image of the printed design, at least aportion of a nozzle pattern and at least a portion of a uniformitypattern.

In accordance with another aspect of the disclosed technique, there isthus provided a method of identifying at least one malfunctioning nozzlein a digital printing press. The digital printing press includes aplurality of nozzles. The method includes the procedures of printing atleast one of at least a portion of a nozzle pattern and at least aportion of a uniformity pattern and acquiring at least one image of theat least one of at least a portion of the nozzle pattern and at least aportion of the uniformity pattern. The method further includes theprocedure identifying the malfunctioning nozzle and classifying the atleast one malfunction nozzle according to the at least one acquiredimage of the at least one of the at least a portion of the nozzlepattern and the at least a portion of the uniformity pattern.

BRIEF DESCRIPTION OF THE DRAWINGS

The disclosed technique will be understood and appreciated more fullyfrom the following detailed description taken in conjunction with thedrawings in which:

FIG. 1 is a schematic illustration of a digital press, withmalfunctioning nozzle detection, constructed and operative in accordancewith an embodiment of the disclosed technique;

FIG. 2 is a schematic illustration of a nozzle pattern for detectingmalfunctioning nozzles in a digital printing press, in accordance withanother embodiment of the disclosed technique;

FIGS. 3A, 3B and 3C, are schematic illustrations of a single nozzlepattern row, in accordance with a further embodiment of the disclosedtechnique;

FIG. 4 is a schematic illustration of a method of identifyingmalfunctioning nozzles in a digital printing press;

FIG. 5 is a schematic illustration of a uniformity pattern constructedand operative in accordance with a further embodiment of the disclosedtechnique;

FIG. 6 is a schematic illustration of a method for determininginconsistency in ink density in a digital printing press, operative inaccordance with another embodiment of the disclosed technique;

FIG. 7 is a schematic illustration of a substrate on which a printeddesign, as well as portions of a pattern are printed in the margins;

FIG. 8 is a schematic illustration of a state machine employed fordetecting malfunctioning nozzles with either a nozzle pattern or auniformity pattern or both, operative in accordance with a furtherembodiment of the disclosed technique; and

FIG. 9 is a schematic illustration of an exemplary method implementingthe above described state machine.

DETAILED DESCRIPTION OF THE EMBODIMENTS

The disclosed technique overcomes the disadvantages of the prior art byproviding a system and methods for identifying a malfunctioning nozzleor a group of nozzles as well as classifying the malfunction. Accordingto the disclosed technique, a malfunctioning nozzle is identified byprinting one or both of a nozzle pattern and a uniformity pattern. Thenozzle pattern enables detection at least one of missing nozzles,deviated nozzles, inconsistent nozzles and redundant nozzles. Suchnozzles may cause streaks to appear on the printed design. Theuniformity pattern enables detection of a nozzle or group of nozzleswhich deposit more or less ink than intended resulting in shades of thecolor being printed across the substrate rather than a uniform color.The term ‘nozzle’ relates herein to a discrete ink deposition unit whichdeposits a dot of ink on a substrate. The term ‘identifying amalfunctioning nozzle’ or ‘identifying a nozzle’ relates to identifyingthe location of the nozzle in a nozzle array. The term ‘location of thenozzle’ or ‘nozzle location’ relates to the location of the nozzle(e.g., the index number, bus address and the like) in the array ofnozzles.

Reference is now made to FIG. 1, which is a schematic illustration of adigital press, generally referenced 100, with malfunctioning nozzledetection, constructed and operative in accordance with an embodiment ofthe disclosed technique. Printing press 100 includes a nozzle bank 102,an imager 104, a processor 106 and a memory 107. Imager 104 includes asensor 108. Sensor 108 includes a plurality of pixels sensors such aspixels 110. Processor 106 is coupled with nozzle bank 102, with memory107 and with imager 104.

Imager 104 is for example a line-scan camera (i.e., which includes aline of pixel sensors) or a contact imager sensor (CIS) which acquires agrey level image or a color image (e.g., a Red Green and Blue—RGBimage). Imager 104 may also be an area camera (i.e., which includes amatrix of pixel sensors). When imager 104 is a color imager, imager 104includes an acquisition channel (e.g., at least one line of sensors orat least one respective illumination) for each acquisition color of theimager 104. Nozzle bank 102 includes an array of nozzles, which includesa plurality of nozzle lines each nozzle line includes a plurality ofnozzles. Each of at least one nozzle line is associated with arespective color to be printed. In other words each color is printed bya respective nozzle line or lines. A Nozzle line or lines which print arespective color is also referred to herein as a ‘color unit’.

When one or more of the nozzles, such as nozzles 114 and 116, aremalfunctioning, respective artifacts, such as streaks 118 and 120, mayappear on the printed design and consequently in an image of the printeddesign acquired by imager 104. A streak may be a ‘negative streak’(i.e., when a nozzle deposits less ink than intended) or a ‘positivestreak’ (i.e., when a nozzle deposits more ink than intended) or a‘color streak’ (i.e., a streak of the wrong color). Identifying themalfunctioning nozzles (also referred to as defective nozzles) as wellas the type of malfunction (i.e., classifying the malfunction) isimportant to ensure the quality of the printed product. However, ingeneral, the number of pixels sensors in sensor 108 may be smaller thanthe number of nozzles in each nozzle line (i.e., the resolution of theimager is smaller than then resolution of the digital printing press100). Thus, more than one nozzle is associated with each pixel. Evenwhen the number of pixels of imager 104 is equal or larger thanresolution of printing press 100, the nozzles do not necessarilycoincide with the pixels sensor in sensor 108 both in terms of alignment(i.e., a nozzle may print a respective dot in an area on the substrate,a portion of which is covered by one pixel sensor and the other portionof which is covered by an adjacent pixel sensor) and in terms of dotwidth (i.e., the dot width may be larger than the width covered by onepixel sensor)

According to an embodiment of the disclosed technique, identifying themalfunctioning nozzle as well classifying the malfunction is achieved byprinting a nozzle pattern and analyzing the acquired image of thatpattern. Reference is now made to FIG. 2, which is a schematicillustration of a nozzle pattern, generally reference 150, for detectingmalfunctioning nozzles in a digital printing press, in accordance withanother embodiment of the disclosed technique. Nozzle pattern 150includes a respective nozzle color pattern for each color being printed.In the example brought forth in FIG. 2, nozzle pattern 150 includesthree nozzle color patterns 152, 154 and 156, also referred to as‘blocks’, each respective of a color being printed. Each one of nozzlecolor patterns 152, 154 and 156 is associated with a respective block ofnozzles (i.e., a line or lines of nozzles printing the same color). Eachone of nozzle color patterns 152, 154 and 156 includes respective nozzlepattern rows printed across the substrate (i.e., perpendicular to thedirection of motion of the substrate). Nozzle color pattern 152 includesnozzle pattern rows 158 ₁, 158 ₂, . . . , 158 _(n). Nozzle color pattern154 includes nozzle pattern rows 160 ₁, 160 ₂, . . . , 160 _(n) andNozzle color pattern 156 includes nozzle pattern rows 162 ₁, 162 ₂, . .. , 162 _(n).

As mentioned above, the number of pixels in sensor 108 may be smallerthan the number of nozzles in each nozzle line. In order to have asingle nozzle mark associated with at least one pixel, each nozzlepattern row is associated with unique respective nozzles, printingrespective nozzle marks (e.g., nozzle marks 164 and 166), such that eachnozzle in each row is spaced apart by a determined number of nozzles.For example, the nozzle marks in nozzle pattern rows 158 ₁ 158 ₂, . . ., 158 _(n) of nozzle color pattern 152 are spaced apart by four nozzles.In other words line 158 ₁ is associated with nozzles 1, 5, 9, . . . ,n−4 which print respective nozzle marks. Line 158 ₂ is associated withnozzles 2, 6, 10, . . . , n−3 which print respective nozzle marks. Line158 _(n) is associated with nozzles 4, 8, 12, . . . , n which printrespective nozzle marks. Similarly, the nozzle marks in nozzle patternrows 160 ₁ 160 ₂, . . . , 160 _(n) of nozzle color pattern 154 arespaced apart by four nozzles and the nozzle marks in nozzle pattern rows162 ₁ 162 ₂, . . . , 162 _(n) of nozzle color pattern 154 are spacedapart by four nozzles. It is noted that nozzle pattern 150 is broughtherein as an example of a nozzle pattern.

In general, a nozzle pattern includes a nozzle color pattern for eachprinted color. Each nozzle color pattern includes a plurality of nozzlepattern rows. Each row represents a sampling of 1 in K nozzlesrepresented by the vertical marks (i.e., K is the sampling period inunits of nozzles) printed in the respective color. Each row is shiftedby an offset of 1 nozzle. In other words, in each block, row i/K printsnozzles i, i+k, i+2 k etc. The nozzles in each color unit areinterleaved by one nozzle over K rows resulting in a full coverage ofthe nozzles of the color unit. In the example brought forth herein inconjunction with FIG. 2, K=4. K is determined according to theresolution of imager 104, such that single nozzle marks can be discernedand segmented in an image acquired by imager 104 and maximal expecteddeviation (E.g. if a nozzle deviates such that it ‘jumps over thehurdle’ of its i±k adjacent segments, an ambiguity may ensue).

Further included in a nozzle pattern 150 are nozzle locators forassociating between nozzle marks in the acquired image of nozzle pattern150 and the nozzles which printed that nozzle mark. Each one of nozzlepattern rows 158 ₁, 158 ₂, . . . , 158 _(n) includes a respective nozzlelocator 168 ₁, 168 ₂, . . . , 168 _(n). Similarly, each one of nozzlepattern rows 160 ₁, 160 ₂, . . . , 160 _(n) includes a respective nozzlelocator 170 ₁, 170 ₂, . . . , 170 _(n) and each one of nozzle patternrows 162 ₁, 162 ₂, . . . , 162 _(n) includes a respective nozzle locator172 ₁, 172 ₂, . . . , 172 _(n). Each one of nozzle locators 168 ₁, 168₂, . . . , 168 _(n), 170 ₁, 170 ₂, . . . , 170 _(n), 172 ₁, 172 ₂, . . ., 172 _(n) is printed by a predetermined respective set of nozzles inthe respective nozzle pattern row thereof and exhibit a respectiveshape. In the example brought forth in FIG. 2, all of nozzle locators168 ₁, 168 ₂, . . . , 168 _(n), 170 ₁, 170 ₂, . . . , 170 _(n) and 172₁, 172 ₂, . . . , 172 _(n) exhibit a rectangular shape. However, anozzle locator according to the disclosed technique may exhibit anypre-defined shape (i.e., geometrical such as a triangle, a square, acircle or an ellipse or an arbitrary shape which bay be defined in animage space according to pixels associated therewith). In general,nozzle locators 168 ₁, 168 ₂, . . . , 168 _(n), 170 ₁, 170 ₂, . . . ,170 _(n) and 172 ₁, 172 ₂, . . . , 172 _(n) as well as the nozzle marksare identifiable in an image acquired by an imager such as imager 104(FIG. 1). Since nozzle locators 168 ₁, 168 ₂, . . . , 168 _(n), 170 ₁,170 ₂, . . . , 170 _(n) and 172 ₁, 172 ₂, . . . , 172 _(n) were printedby a predetermined respective set of nozzles and the spacing between thenozzle marks in is also known, a processor (e.g., processor 106—FIG. 1)can associated between each nozzle mark in an image and the nozzle whichprinted that nozzle mark as further explained below.

To identify a malfunctioning nozzle, imager 104 acquires an image of thenozzle pattern and provides this image to processor 106. Furthermore,memory 107 provides processor 106 with information relating to thenumber of nozzle color patterns (i.e., blocks) in the nozzle pattern,the number of nozzle pattern rows in each block, the nozzle samplingperiod K, the height of each nozzle mark, the resolution of digitalpress 100 (e.g., 1200 Dots Per Inch−Dpi), the ‘x-deviation threshold’and the ‘strength score threshold’ (the latter two are further explainedbelow).

Processor 106 analyzes the image of the nozzle pattern. For each row,processor 106 determines the number of the reference nozzle (e.g., thenozzle left of the nozzle locator), a list of the nozzles numbersassociated with the nozzle marks detected and the index of each mark inthe row. Processor 106 further determines the deviation of the nozzlemark from the expected location of the nozzle mark (e.g., in millimetersor in pixel units), a strength score (e.g., a score between 0 to 1) andoptionally nozzle classification (e.g., intact, missing, deviated,inconsistent, redundant as further explained below).

Initially, processor 106 segments the acquired image into the differentnozzle color patterns (i.e., blocks) for example, according to thelocation of the nozzle color pattern in the acquired image andoptionally according to the color of the nozzle color pattern. Processor106 further segments each block into rows and each row into nozzle marksand nozzle locators. The nozzle marks identified from the segmentedimage of the nozzle pattern are referred to herein as ‘detected nozzlemarks’. Detecting and classifying a malfunctioning nozzle or nozzlesemploying a nozzle pattern is explained with the example of a singlenozzle pattern row. It is however noted that this explanation relates toeach of the nozzle pattern lines in the nozzle pattern.

Reference is now made to FIGS. 3A, 3B and 3C, which are schematicillustrations of a single nozzle pattern row, generally referenced 180,in accordance with a further embodiment of the disclosed technique andreferring also to FIG. 1. Nozzle pattern row 180 is similar to nozzlepattern rows 158 ₁, 158 ₂, . . . , 158 _(n), 160 ₁, 160 ₂, . . . , 160_(n), 162 ₁, 162 ₂, . . . , 162 _(n) described hereinabove inconjunction with FIG. 2. Nozzle pattern row 180 includes 17 nozzle marks182 ₁, 182 ₅, 182 ₉, 182 ₁₃, 182 ₁₇, 182 ₂₁, 182 ₂₅, 182 ₂₉, 182 ₃₃, 182₃₇, 182 ₄₁, 182 ₄₅, 182 ₄₉, 182 ₅₃, 182 ₅₇, 182 ₆₁ and 182 ₆₅respective, for example, of each fourth nozzle in a line of nozzles(i.e., K=4) in a digital printing press, where the subscripts relate tothe nozzle number printing the mark. Furthermore, nozzle pattern row 180includes a nozzle locator 184 similar to nozzle locators 168 ₁, 168 ₂, .. . , 168 _(n), 170 ₁, 170 ₂, . . . , 170 _(n) and 172 ₁, 172 ₂, . . . ,172 _(n) described hereinabove in conjunction with FIG. 2. Nozzlelocator 184 is printed by nozzles number 33, 34, 35, 36 and 37, wherethe height of the marks printed by nozzles 34, 35 and 36 is a portion ofthe height of nozzle mark 182 ₃₃, 182 ₃₇. In the example brought forthin FIGS. 3A, 3B and 3C, nozzle locator 184 exhibits the shape of arectangle and is identifiable in an image acquired by imager 104 (FIG.1).

To detect and classify a malfunctioning nozzle, initially processor 106determines the center of gravity (i.e., the average location of thepixels in the segments) of each segment associated with a detectednozzle mark. Processor 106 employs this center of gravity as thelocation reference of the nozzle mark in the image. Furthermore,processor determines the ‘strength score’ of the nozzle mark, forexample, by averaging the detected intensity level of each pixel in thesegment associated with the nozzle mark. Also, processor 106 determinesa local grid for a selected group of expected consecutive nozzle marks.The selected group of expected consecutive nozzle marks is also referredto herein as ‘the grid window’. For example, in FIG. 3A, processor 106determines a local grid for nozzle marks 182 ₅, 182 ₉, 182 ₁₃, 182 ₁₇and 182 ₂₁ such that each one of nozzle marks 182 ₅, 182 ₉, 182 ₁₃, 182₁₇ and 182 ₂₁ is associated with a respective location in the localgrid. To determine the local grid, processor 106 determines the spacing(i.e., the relative location) between consecutive detected nozzle marksin the selected group of nozzle marks. Thereafter, processor 106determines the grid spacing (i.e., the distance between the grid points)that best fits the spacing between the nozzle marks, for example,according to the least square criterion. According to the least squarecriterion, the spacing between grid points is determined such that thesum of squared differences between the grid points and the nozzle marksare minimized. The start of the grid is anchored, for example, at adetermined distance before the first nozzle mark in the group (e.g., athalf the expected distance between the first nozzle mark in the groupand the preceding nozzle mark). In other words, processor 106 determinesthe coefficients of the equations:

y=ax+b  (1)

where a is the grid spacing and b is the anchor point before the firstnozzle mark in the group. It is noted that the term ‘distance’ hereinrefers to the selected metric employed to determine the spacing, whichcan be measured, for example, in millimeters or pixel units. It is alsonoted that a special case of a local grid, such as described hereinabove, is the global grid, where the selected group of nozzle marksincludes all of the detected nozzle marks in the row. It is furthernoted the total length (e.g., in millimeters or in pixel units) of thedetermined local grid may be different for each local grid since thedetermined grid spacing (i.e., a in equation (1)) is different. Also,the grid window is determined such that the probability that the nozzlesprinting in the grid windows malfunction, but that these nozzle marksshall appear to relate to intact nozzles (i.e., as further explainedbelow), is below a determined threshold. It is noted that this thresholdalso relates to the aberrations in the imager optics (i.e., since suchaberrations may cause a nozzle mark to appear in the image in adifferent location than the location in which the nozzle mark isactually located).

Once local grid 186 is determined, processor 106 employs local grid 186to detect and classify malfunctioning nozzles. Processor 106 projectsthe determined local grid 186 on the image or the segment of the imagewhich includes the selected group of expected consecutive nozzles. Todetermine a malfunctioning nozzle, processor 106 determines the expectedlocation of each nozzle mark on local grid 186 (i.e., expected nozzlemark location), according to the resolution of digital press 100, thenozzle sampling rate K and the number of grid points. For example, andwith reference to FIG. 3B, with a printing press resolution of 101.6dpi, K=4 (i.e., which results in an expected spacing of 1 millimeterbetween nozzle marks) and 40 grid points, processor 106 determines thatthe expected nozzle mark location are spaced apart by 8 grid points. Byanchoring the first grid point at half the expected distance between thefirst nozzle mark and the preceding nozzle mark, in FIG. 3B, processor106 determines that grid locations 188 ₁, 188 ₂, 188 ₃, 188 ₄ and 188 ₅correspond to the expected nozzle mark locations. FIG. 3B exhibits anenlarged view of nozzle marks 182 ₁, 182 ₅, 182 ₉, 182 ₁₃, 182 ₁₇, 182₂₁ and 182 ₂₅ and local grid 186 where nozzle marks 182 ₁, 182 ₁₇ aremissing and nozzle mark 182 ₉ has deviated. The expected location ofnozzle marks 182 ₁, 182 ₉ and 182 ₁₇ in nozzle pattern row 180 aremarked with a dotted line. The grid locations corresponding to theexpected location of nozzle marks 182 ₅, 182 ₉, 182 ₁₃, 182 ₁₇ and 182₂₁ are depicted as thickened lines 188 ₁, 188 ₂, 188 ₃, 188 ₄ and 188 ₅.An expected location of a nozzle mark, also referred to as ‘grid slot’.As mentioned above, processor 106 projects the determined local grid 186on the image or the segment of the image which includes the selectedgroup of expected consecutive nozzles, for example, by determining thepixels corresponding to the grid slots in the image relative to thedetected nozzle marks. Furthermore, processor 106 associates eachdetected nozzle mark (i.e., the location of the center of gravity of thesegment with the nozzle mark) with a respective location on local grid186 (i.e., the actual nozzle mark location). Furthermore, processor 106associates each detected nozzle mark with the closest expected nozzlemark location thereto and determines the distance, d_(x), therebetween.

A nozzle may be classified as ‘intact’ when a nozzle mark is identifiedin an expected nozzle mark location (i.e., within a determinedtolerance). A nozzle may be classified as ‘missing’ if an expectednozzle mark location is not associated with a detected nozzle mark. Anozzle may be classified as ‘deviated’ if the distance, d_(x), betweenthe actual nozzle mark location and the expected nozzle mark locationassociated with the detected nozzle mark is above a threshold distancereferred to herein also as the ‘x-deviation threshold’. A nozzle may beclassified as an ‘inconsistent nozzle’ (i.e., in terms of dot size andposition consistency relative to previous dots) if the respectivedetected nozzle mark thereof exhibits a strength score above or below adetermined ‘strength score threshold’. The strength score threshold maybe determined according to the statistics of the strength scores of theselected group of consecutive nozzle marks (e.g., below the average ofthe strength scores, or below the average minus the standard deviationof the strength scores and the like). A nozzle is classified as a‘redundant nozzle’ when a nozzle mark is detected between two expectednozzle mark locations with detected nozzle marks associated therewith.

In general, a nozzle mark may be included in more than one selectedgroup of nozzle marks (i.e., at least two groups of selected nozzlemarks at least partially overlap). For example, the groups of nozzlemarks are selected according to a sliding window over the detectednozzle marks starting from the first detected nozzle mark at a selectedside of the row, where the sliding windows move toward the other side ofthe row. The step size of sliding window (i.e., the number of nozzlesmarks between the start of each window) such that each nozzle mark isincluded in at least one local grid and preferably in two or more localgrids. When a nozzle mark is included in more than one group of selectednozzle marks, the detected information relating thereto (e.g., thedifference between the expected and detected location) may be averaged,thus reducing the probability of miss detection and of determiningerroneous information. For example, with reference to FIG. 3C, a localgrid 190 is fitted to selected nozzle marks 182 ₉, 182 ₁₃, 182 ₁₇, 182₂₁ and 182 ₂₅. Accordingly, nozzle marks 182 ₉, 182 ₁₃, 182 ₁₇ and 182₂₁ are included the selected group of nozzle marks to which both localgrid 186 and local 188 were fitted. Thus, for example, the detecteddeviation of nozzle mark 182 ₉, may be averaged. It is also noted thatemploying a local grid fitted to the spacing between a selected group ofconsecutive detected nozzle marks, reduces the effects on the acquiredimage of optical aberrations (e.g., local lens barrel/pincushiondistortions) or motion of the printed substrate (e.g., a sheet or acontinuous web) or both.

In some situation, the first or the first consecutive (e.g., the firsttwo, the first three etc.) nozzle marks may be missing. In such a case,when a sliding window is employed, the first missing nozzles may not bedetected since processor 106 does not detect a nozzle mark correspondingthereto. To identify such missing nozzles, processor 106 employs theresults of the above mentioned segmentation and identifies nozzlelocator 184. Also, as mentioned above, memory 107 provides processor 106is with information relating to which nozzles printed nozzle locator184. Accordingly, processor 106 can associate nozzle mark 182 ₂₉ withnozzle 29 or mark 182 ₃₃ with nozzle 33 or both. Thus, either nozzlemark 182 ₂₉ or nozzle mark 182 ₃₃ or both can be employed as referencenozzle marks. The information relating to the number of nozzles in eachrow (i.e., the expected number of nozzle marks) and the location ofnozzles that printed nozzle locator 184 as well as nozzle marks mark 182₂₉ or 182 ₃₃ are available to processor 106. Processor 106 can determinethe number of nozzle marks detected to the left and right of thereference nozzle mark. The difference between the number of detectednozzle (i.e., including the missing nozzles) and the expected number ofnozzles is the number of first consecutive missing nozzles. It is notedthat printing a nozzle locator, such as nozzle locator 184, with aplurality of consecutive nozzles, enables processor 106 to identifynozzle locator 184 even in the event of some of these nozzles aremissing, since the size and shape of nozzle locator 184 are known. Forexample, processor 106 identifies a segment in the acquired image whichmatches a locator pattern template according to an image similaritymeasure (e.g., normalized cross correlation) between the template andthe segment.

Also, the location of nozzles that printed nozzle locator 184 as well asthe location of the reference nozzle marks are available to processor106. Thus, processor 106 can identify each of the nozzles (e.g.,determine the index or the bus address of the nozzles) in each nozzlepattern row according to the location of the reference nozzle marks andK. In other words, nozzle locator serves as a registration mark betweenthe printed nozzle marks and the nozzles which printed those marks.Since nozzle marks may be identified as missing, the location of themissing nozzles can also be determined.

Reference is now made to FIG. 4, which is a schematic illustration of amethod of identifying malfunctioning nozzles in a digital printingpress. In Procedure 200, a nozzle pattern is printed on a web or on asheet. The nozzle pattern includes a respective nozzle color pattern foreach color being printed. Each one of the nozzle color patterns isassociated with a respective block of nozzles (i.e., a line or lines ofnozzles printing the same color). Each nozzle pattern row is associatedwith unique respective nozzles, printing respective nozzle marks, suchthat each nozzle in each line is spaced apart by a determined number ofnozzles. With reference to FIGS. 1 and 2, digital printing press 100prints a nozzle pattern such as nozzle pattern 150.

In procedure 202, an image of the printed nozzle pattern is acquired.With reference to FIGS. 1 and 2, imager 104 acquires an image of nozzlepattern 150.

In procedure 204, nozzle marks and at least one nozzle locator aredetected in each nozzle pattern row of a respective nozzle colorpatterns in the nozzle pattern. To identify the nozzle marks and thenozzle locator or locators, the acquired image is segmented into thedifferent nozzle color patterns. The nozzle color patterns are furthersegmented into respective nozzle pattern rows and each nozzle patternline is further segmented into respective nozzle marks and nozzlelocators. With reference to FIGS. 1 and 2, processor 106 identifiesnozzle marks such as nozzle marks 114 and 114 in nozzle pattern row 158₁ of nozzle color pattern 152 in nozzle pattern 150.

In procedure 206, a grid is determined for a selected group ofconsecutive nozzle marks including expected nozzle mark locations andthe actual nozzle mark location. The grid is determined, for example, bydetermining the grid spacing and anchor point which best fit the spacingbetween the selected group of consecutive nozzle marks as describedabove in conjunction with FIGS. 3A-3C. The actual nozzle mark locationand the expected locations of nozzle marks on the grid are alsodetermined also as described above in conjunction with FIGS. 3A-3C. Thedistance between With reference to FIG. 1, 3B, processor 106 determinesa grid, such as local grid 186, for consecutive nozzle marks 182 ₅, 182₉, 182 ₁₃ and 182 ₂₁ where nozzle mark 182 ₁₇ is missing. Thickenedlines 188 ₁, 188 ₂, 188 ₃, 188 ₄ and 188 ₅ represent the expectedlocation of nozzle marks 182 ₅, 182 ₉, 182 ₁₃, 182 ₁₇ and 182 ₂₁

In procedure 208, each expected nozzle mark location is associated witha respective detected nozzle mark and the distance, d_(x), between theexpected nozzle mark location and the actual nozzle mark location (i.e.,of the respective nozzle mark) is determined as described above inconjunction with FIGS. 3A-3C. As described above each nozzle mark (i.e.,the location of the center of gravity of the segment associated with thenozzle mark) is associated with a respective location on the local grid.Furthermore, each nozzle mark is associated with the closest expectednozzle mark location thereto and the distance, d_(x), between the actualnozzle mark location and the expected nozzle mark location isdetermined. With reference to FIG. 1, processor 106 associates eachexpected nozzle mark location with a respective nozzle mark anddetermines the distance between the expected nozzle mark location andthe actual nozzle mark location.

In procedure 210, nozzles are identified and classified according to theexpected nozzle mark location on the grid, the actual nozzle marklocation on the grid, the nozzle locator and the nozzle sampling periodK. A nozzle may be classified as ‘intact’ when a nozzle mark isidentified in an expected location of a nozzle mark location (i.e.,within a determined tolerance). A nozzle may be classified as ‘missing’when an expected nozzle mark location is not associated with a detectednozzle mark. A nozzle may be classified as ‘deviated’ when the distance,d_(x), between the actual nozzle mark location and the expected nozzlemark location associated with the detected nozzle mark is above thex-deviation threshold. A nozzle may be classified as an ‘inconsistentnozzle’ when the respective nozzle mark thereof exhibits respectivestrength score below the strength score threshold. A nozzle may beclassified as a ‘redundant nozzle’ when a nozzle mark is detectedbetween two expected nozzle mark locations with nozzle marks associatedtherewith. Furthermore, the first or the first consecutive missingnozzles are identified by identifying a reference nozzle mark in thenozzle locator. Since the number of nozzles printing in each row isknown and the number of nozzle marks to the left and right of thereference nozzle mark are also determined, the difference between thenumber of detected nozzle (i.e., including the missing nozzles) and theexpected number of nozzles is the number of first consecutive missingnozzles. Also, the location of nozzles that printed the nozzle locator,as well the location of the reference nozzle marks are known. Thus, eachof the nozzles in each nozzle pattern row can be identified (e.g.,determine the index or the bus address of the nozzles) according to thelocation of the reference nozzle marks (as determined from the nozzlelocator) and K. Since nozzle marks may be identified as missing, thelocation of the missing nozzles can also be determined. With referenceto FIG. 1, processor 106 identifies and classifies malfunctioningnozzles.

The nozzle pattern described above in conjunction with FIGS. 2, 3A-3Cand 4 enables detecting a missing nozzle, a deviated nozzle, aninconsistent nozzle and a redundant nozzle. However, each nozzle maydeposit a different amount of ink for a given dot size (e.g., the dotdiameter), resulting in inconsistency in the dot size printed on thesubstrate. The dot size printed on the substrate is also referred toherein as ‘coverage’, ‘ink density’ or just ‘density’. Inconsistency inthe dot size may result from different electrical characteristics (e.g.,different resistances, capacitance and the like) of the mechanism, alsoreferred to as ‘nozzle head’, of the nozzle printing the dot.

Such inconsistency in the dot size printed on the substrate may resultin different shades of color being printed rather than a uniform shadeof color. This phenomenon is also known as ‘banding’ and may affect theprinted quality of the design. Accordingly, it would be beneficial todetermine which nozzles or group of nozzles deposit a different amountof ink for a given dot size. To that end a uniformity pattern is printedon the substrate. It is noted that the uniformity pattern is differentfrom the nozzle pattern described above. Reference is now made to FIG.5, which is a schematic illustration of a uniformity pattern generallyreferenced 250, constructed and operative in accordance with a furtherembodiment of the disclosed technique. Uniformity pattern 250 includes aplurality of color uniformity patterns respective of each color beingprinted. In the example brought forth in FIG. 5, uniformity pattern 250includes four color uniformity patterns, color uniformity pattern 252respective of the color black, color uniformity pattern 254 respectiveof the color magenta, color uniformity pattern 256 respective of thecolor cyan, color uniformity pattern 258 respective of the color yellow.Each one of color uniformity pattern, 252, 254, 256 and 258 includes aplurality of color uniformity rows printed across the substrate. Eachcolor uniformity row in each color uniformity pattern is associated witha respective different planned ink density level (i.e., the densityintended to be printed). Furthermore, respective color uniformity rowsin each color uniformity patterns may be associated with the same inkdensity levels different form the ink density levels of other respectiverows. In the example brought forth in FIG. 5, each one of coloruniformity pattern, 252, 254, 256 and 258 includes four color uniformityrows respective of the same four different ink density levels. Coloruniformity pattern 252 includes color uniformity row 252 ₁ associatedwith very high planned density (e.g., a value between 85% to 100% of thelargest possible dot size on the substrate), color uniformity row 252 ₂associated with high planned density (e.g., a value between 60% to 75%of the largest possible dot size on the substrate), color uniformity row252 ₃ associated with medium planned density (e.g., a value between 35%to 50% of the largest possible dot size on the substrate) and coloruniformity row 252 ₄ associated with low planned density (e.g., a valuebetween 10% to 25% of the largest possible dot size on the substrate).Similarly, color uniformity pattern 254 includes color uniformity row254 ₁, 254 ₂, 254 ₃ and 254 ₄ associated with very high, high, mediumand low densities respectively, color uniformity pattern 256 includescolor uniformity row 256 ₁, 256 ₂, 256 ₃ and 256 ₄ also associated withvery high, high, medium and low densities respectively, color uniformitypattern 254 includes color uniformity row 258 ₁, 258 ₂, 258 ₃ and 258 ₄which are also associated with very high, high, medium and low densitiesrespectively. It is noted that the planned density level printed by eachcolor uniformity row may be determined, for example, according to theparameters of the printing press such as dot gain resolution, dpi andthe like. It is further noted, all of the nozzle associated with therespective color are employed in printing color uniformity rows 252₁-252 ₄, 254 ₁-254 ₄, 256 ₁-256 ₄ and 258 ₁-258 ₄.

As mentioned above, each nozzle may deposit a different amount of inkfor a given dot size, resulting in inconsistency in the dot size printedon the substrate. In FIG. 5, the nozzles printing the color black allprint the same dot size. As such color uniformity pattern 252 exhibits auniform shade for color uniformity rows 252 ₁, 252 ₂, 252 ₃ and 252 ₄.With regards to the color magenta, a portion of the nozzles printing thecolor magenta print a larger dot size. As such, a section 260 in eachcolor uniformity rows 254 ₁, 254 ₂, 254 ₃ and 254 ₄ exhibit a darkershade. With regards to the color cyan, a portion of the nozzles printingthe color cyan print a smaller dot size. As such, a section 262 in eachcolor uniformity rows 256 ₁, 256 ₂, 256 ₃ and 256 ₄ exhibit a lightershade. With regards to the color yellow, a portion of the nozzlesprinting the color yellow print a larger dot size. As such, a section264 in each color uniformity rows 258 ₁, 258 ₂, 258 ₃ and 258 ₄ exhibita darker shade.

To determine which nozzles prints a different dot size and referringalso to FIG. 1, digital printer 100 prints a uniformity pattern such asuniformity pattern 250. Imager 104 acquires an image of the printeduniformity pattern and provides the acquired image to processor 106.Processor 106 is provided with information relating to the number ofcolors being printed, the number of rows in each color uniformitypattern, the height of each row, the resolution of the digital press,the resolution of the imager, and the spacing between each the rows ineach color uniformity pattern and the spacing between the coloruniformity patterns. Processor 106 provides a map (e.g., a twodimensional Look Up Table) of the relative intensities of the rows asfurther explained below.

Processor 106 segments the acquired image into color uniformitypatterns. Processor 106 further segments each color uniformity patterninto color uniformity rows. Processor 106 determines the relativedensity of each bin (i.e., pixel or group of pixels depending on theresolution of the imager relative to the resolution of the digitalprinting press) across each color uniformity row. The relative densityis determined according to the following:

d=[i _(x)]=log I[i _(x) ]/I _(o)

where i_(x) relates to the bin number, I[i_(x)] relate to the detectedintensity of the row (i.e., as detected by imager 104), where thesubscript x represents the fact that the measurements are made acrossthe substrate and I_(o) relates to the detected intensity of thesubstrate (i.e., also as detected by imager 104). By determining therelative densities across the bins in one of color uniformity rows 258₁, 258 ₂, 258 ₃ and 258 ₄ of the uniformity pattern 250, processor 106determines a color uniformity map where each bin at each row isassociated with a respective relative density. The map may be in theform of a two dimensional LUT where the rows correspond to the coloruniformity rows and the columns corresponds to the bins. Processor 106can determine the compensation required at each bin to achieve uniformcolor density according to the difference or the ration between theplanned density (i.e., the density intended to be printed) and detecteddensity in each bin. Since, in general, all the nozzles print theuniformity pattern, each of the bins is associated with a respectivenozzle or group of nozzles. Thus when the bin associating non-uniformityis identified, the nozzles printing that non-uniform bin are alsoidentified.

Reference is now made to FIG. 6, which is a schematic illustration of amethod for determining inconsistency in ink density in a digitalprinting press, operative in accordance with another embodiment of thedisclosed technique. In procedure 280, a color uniformity pattern isprinted on a substrate. The Uniformity pattern includes a plurality ofcolor uniformity patterns respective of each color being printed. Eachcolor uniformity pattern includes a plurality of color uniformity rowsprinted across the substrate. Each color uniformity row is associatedwith a respective of a different planned ink density level. Withreference to FIGS. 1 and 5, digital printing press 100 (FIG. 1) printsuniformity pattern 250 (FIG. 5).

In procedure 282, an image of the printed color uniformity pattern isacquired. With reference to FIGS. 1 and 5, imager 104 (FIG. 1) acquiresan image of uniformity pattern 250 (FIG. 5).

In procedure 284, color uniformity rows are identified in the acquiredimage. The color uniformity rows are identified by segmenting theacquired image of the uniformity pattern. With reference to FIGS. 1 and5, processor 106 identifies color uniformity rows 252 ₁-252 ₄, 254 ₁-254₄, 256 ₁-256 ₄ and 258 ₁-258 ₄.

In procedure 286, the relative density of each bin in each coloruniformity row is determined. Each bin relates to a pixel or a group ofpixels in the image depending on the resolution of the imager relativeto the resolution of the digital printing press. With reference to FIGS.1 and 5, processor 106 determines the relative density in each bin ineach of color uniformity rows 252 ₁-252 ₄, 254 ₁-254 ₄, 256 ₁-256 ₄ and258 ₁-258 ₄.

In procedure 288, a required correction for each bin is determined,which exhibits a non-uniform relative density. The required correctionis determined such that uniform density is achieved for at each rowaccording to the difference or the ration between the planned densityand detected density in each bin. With reference to FIG. 1, processor106 determines the correction required at each bin to achieve uniformcolor density according to the difference or the ration between theplanned density and detected density in each bin.

The above described uniformity pattern may also be employed foridentifying the presence of a missing nozzle, a deviated nozzle, aninconsistent nozzle or a redundant nozzle. As mentioned above, when atleast one of the nozzles is either missing, deviated inconsistent orredundant, a streak may appear on the printed substrate and consequentlyin the acquired image. Accordingly, referring to FIG. 1, processor 106identifies such a streak in an acquired image of the uniformity pattern.When processor 106 identifies such a streak in the image (e.g., byidentifying regions of reduced density in the density uniformity patternor identifying segments in the image exhibiting elongated shape), theprocessor 106 determines the group of nozzles from which the streakoriginated (i.e., the suspected nozzles) according to the location ofthe streak in the image. Processor 106 can then instruct digital press100 to print a nozzle pattern for only a portion of the nozzles whichincludes the suspected nozzles (i.e., the number of nozzles in theportion printing the nozzle pattern may be larger than the number ofsuspected nozzles). Thus, the processing time required to identify thenozzle causing the streak as well as substrate waste may be reduced.

When employing a nozzle pattern or a uniformity pattern such asdescribed above, the acquired images of these patterns are converted togray level imagers (i.e., for reducing the required processing thereof).One alternative for converting a color image such as a RGB image into agrey scale image is averaging the intensities of the Red Green and Bluechannels. However, since the appearance of color in an image is alsoaffected by the background, simply averaging the intensities may lead toa gray level image which does reflect the relative intensities of thecolor image (e.g., when printing yellow, the average or RGB intensityvalues may render the intensity of yellow as low relative to theintensity of the background). Furthermore, when associating a specificprinciple channel (e.g., Red Green or Blue) of a selected color (e.g. weanalyze the ‘Cyan’ uniformity pattern over the ‘red’ channel of theimager), for colors such as orange, green, violet or spot colors such asPantone colors, the intensity values from more than one acquisitionchannel may be needed to determine the relative intensity of the color.To avoid such occurrences, according to the disclosed technique, andwith reference to FIGS. 5 and 1, for each color, processor 106 selectsfrom the color uniformity row with the maximum coverage, an area in theimage, such as area 266, which includes substantially the same number ofcolor pixels and background pixels. Processor 206 determines thestandard deviation of the intensity levels of the pixels for that coloraccording to the intensities of the pixels in the selected area.Processor 106 than weighs each of the determined standard deviations ofeach color (e.g., by dividing the determined standard deviation of theintensities of each color by the sum of determined standard deviations),and determines the weighted average of the RGB intensities at eachchannel, for each color, to determine the grey level for that color.

In some cases, it would not be possible to print an entire pattern suchas the above described nozzle pattern or uniformity pattern. Thepartition of these patterns into rows, enables printing only portions ofthese patterns, for example, in the top or bottom margins of the printeddesign. These printed portions of the patterns may then be cut out fromthe final product to be delivered. For example, when the digitalprinting press prints labels, a portion of a pattern may be printed onthe dye cut of the label. Reference is now made to FIG. 7, which is aschematic illustration of a substrate, generally referenced 300, onwhich a printed design as well as portions of a pattern (i.e., a nozzlepattern or a uniformity pattern) are printed in the margins. Depicted inFIG. 7 is a plurality of designs 302 ₁, 302 ₂ and 302 ₃. Portions 304 ₁,304 ₂ and 304 ₃ of a pattern for detecting malfunctioning nozzles (i.e.,a portion of the nozzle pattern or a portion of the uniformity pattern)are printed in the margins between designs 302 ₁, 302 ₂ and 302 ₃.Although portions 304 ₁, 304 ₂ and 304 ₃ are depicted in FIG. 7 asnozzle pattern rows, it is noted that portions 304 ₁, 304 ₂ and 304 ₃ ofa pattern may be a single row in a pattern (i.e., either a nozzlepattern rows or a color uniformity row) or a color pattern (i.e., eithera nozzle color pattern or a color uniformity pattern). To acquire animage of the entire pattern, and with reference to FIG. 1, imager 104acquires an image of each one of portions 304 ₁, 304 ₂ and 304 ₃ andprocessor 106 and generates a composite image of the pattern.

Reference is now made to FIG. 8, which is a schematic illustration of astate machine employed for detecting malfunctioning nozzles with eithera nozzle pattern or a uniformity pattern or both, operative inaccordance with a further embodiment of the disclosed technique. Instate 320, the printed image is monitored for streaks or banding orboth. When the printed image is O.K. the state machine remains in state320. If a suspected malfunctioning nozzle is identified in the image,the state machine proceeds to state 322. In state 322, one of a nozzlepattern or a uniformity pattern or both are printed and the statemachine proceeds to state 324. In state 324 the printed pattern ismonitored to identify, classify the malfunctioning nozzle. According toone alternative, the state machine always returns to state 320 afterstate 324. According to another alternative, the state machine returnsto state 322 when the malfunctioning nozzle is identified (as indicatedby the dashed line) to validate the compensation of the defective nozzleor nozzles, and returns to state 320 when the no malfunctioning nozzleis identified.

Reference is now made to FIG. 9, which is a schematic illustration of anexemplary method implementing the above described state machine. Inprocedure 350, a design is printed on a substrate. With reference toFIG. 1, digital printing press 100 prints a design on a substrate.

In procedure 352 at least one image respective of the printed design isacquired. With reference to FIG. 1, imager 104 acquires at least oneimages of the printed design.

In procedure 354, at least one artifact is identified in the acquiredimages. The artifact may be a streak in the images or a band ofnon-uniform shade of color or both. Streaks may be identified bysegmenting the images and detecting elongated segments (e.g., exhibitinga length to width ratio above a determined threshold). Banding may beidentified by selecting in the reference image (e.g., Raster ImageProcessor-RIP images) areas of uniform color, and detecting in theimages resulting from each acquisition channel differences in coloruniformity in those regions. Alternatively, a difference image may bedetermined by subtracting the acquired images from a reference image(e.g., a PDF of the design, an image of the printed design determined asa reference). The difference images may also be segmented to determinedsteaks and banding as described above. With reference to FIG. 1,processor 106 identifies at least one artifact in the acquired images.When artifacts are identified, the method proceeds to procedure 356.When artifacts are not identified, the method returns to procedure 350.

In procedure 356, the colors and the location of the group of nozzlesfrom which the artifact originated is attempted to be determined. Thelocation of the group of malfunctioning nozzles from which the artifactoriginated may be determined by the location of the artifact on theX-axis of the image (i.e., the axis perpendicular to the direction ofmotion of the substrate). Since the resolution of the image may besmaller than the resolution of the printing press and since some of thenozzles at the ends of the nozzle bank may not be printing, the locationof the malfunctioning nozzle causing the artifact can only be known to acertain degree of confidence. This degree of confidence defines thegroup of suspected malfunctioning nozzles. The color of the suspectedmalfunctioning nozzles may be determined by identifying the colors whichthe artifact exhibits, relative to the predicted color at the locationof the artifact (e.g., from a reference image or from neighboringpixels). At the worst case, the group of nozzles determined as the groupof nozzles from which the artifact originated includes nozzles from allthe colors printed by the printing press. Furthermore, registrationfeatures (e.g., designated registration marks or features in the printeddesign) are determined to allow association between the regions in theimage and the nozzles printing those regions. The printing press knowswhich nozzles printed which parts of the printed design and thus whichnozzles printed the registration features. Identifying the location ofthe registration features in the image results in a correspondence(i.e., registration) between the printed image and the nozzles whichprinted the image. With reference to FIG. 1, processor 106 determines atleast one of the color and the group of malfunctioning nozzles causingthe at least one artifact. It is noted that procedure 356 is optional.

In procedure 358, the at least one malfunctioning nozzle is identifiedand the malfunction is optionally classified, according to at least oneof an acquired image of the printed design, at least a portion of anozzle pattern (e.g., nozzle pattern 150—FIG. 2) and at least a portionof a uniformity pattern (e.g., uniformity pattern 250—FIG. 5). Theportion of the nozzle pattern or the uniformity pattern relates to atleast one of the color and the group of malfunctioning nozzles causingthe artifact or artifacts. Identifying and optionally classifying themalfunctioning nozzle according to at least a portion of a nozzlepattern and at least a portion of a uniformity pattern is performed asdescribed above in conjunction with FIGS. 2, 3A-3C, 4, 5 and 6respectively. Identifying the malfunctioning according to an acquiredimage or images of the printed design is further explained below. Withreference to FIG. 1, processor 106 identifies the malfunctioning nozzleaccording to at least one of an acquired image of the printed design, atleast a portion of a nozzle pattern and at least a portion of auniformity pattern. After procedure 358, the method returns to procedure350

As mentioned above malfunctioning nozzles may be identified from anacquired image of the printed design in which an artifact is identified.The image in which the artifact is identified is referred to herein as‘the first artifact image’. According to one alternative, each one ofthe suspected nozzles is turned off (e.g., by turning off the printheads of the digital printer). For each suspected nozzle that is turnedoff, another repetition of design is printed on the substrate and animage thereof is acquired. When a change (e.g., a new streak) occurs inthe newly acquired image relative to the first artifact image, then thenozzle that was turned off is not the malfunctioning nozzle. When nochange occurs in the newly acquired image relative to the first artifactimage, then the nozzle that was turned off is determined to be amalfunctioning nozzle. According to another alternative, each one of thesuspected nozzles is compensated for. For each compensated suspectednozzle, another repetition of design is printed on the substrate and animage thereof is acquired. When no artifacts appear in the newlyacquired image relative to the first artifact image, then themalfunctioning nozzle has been identified and compensated for. At theworst case of both of the above described alternative. An exhaustivesearch is conducted to identify the malfunctioning nozzle or nozzles. Itis also noted that verifying that nozzle compensation was successful mayalso be achieved when the printed design in the next cycle of thedigital printing press is different from the printed design beinginspected (i.e., assuming the new design does not include an intendedstreak at the same location and that the suspected nozzles print in thenew design).

In some cases, the substrate employed for printing is non-opaque. Insuch case, when a pattern (e.g., a uniformity pattern or a nozzlepattern) is printed on the substrate the contrast between the patternand the background may be low, rendering the pattern un-identifiable inthe acquired image thereof. Also, in a hybrid printing press (e.g., aflexographic press followed by a digital press) the pattern may beprinted on the colors printed by the flexographic, which may also renderthe pattern un-identifiable in the acquired image thereof. Since, inmany digital printing press the first color unit prints the color white,according to the disclosed technique, when non-opaque substrates areemployed or in hybrid printing presses, the pattern is printed over alayer of white ink (i.e., printed by the white color unit of the digitalpress) to enable analysis of the pattern.

It will be appreciated by persons skilled in the art that the disclosedtechnique is not limited to what has been particularly shown anddescribed hereinabove. Rather the scope of the disclosed technique isdefined only by the claims, which follow.

1. A method of identifying at least one malfunctioning nozzle in adigital printing press, said digital printing press including aplurality of nozzles, the method comprising the procedures of: printinga design on a substrate; acquiring at least one image of the printeddesign; identifying at least one artifact in the acquired image; andidentifying said malfunctioning nozzle and classifying said at least onemalfunction nozzle according to said at least one of said acquired imageof said printed design, at least a portion of a nozzle pattern and atleast a portion of a uniformity pattern.
 2. The method according toclaim 1, wherein identifying said at least one malfunctioning nozzle andclassifying said malfunction according at least a portion of said nozzlepattern includes the procedures of: printing at least said portion ofsaid nozzle pattern, said nozzle pattern including a nozzle colorpattern for each of at least one printed color, each nozzle colorpattern includes a plurality of nozzle pattern rows, each nozzle patternrow includes a plurality of nozzle marks representing samples of 1 in Knozzles, each row is shifted by an offset of 1 nozzle, each nozzlepattern row further including at least one nozzle locator; acquiring animage of the printed portion of said nozzle pattern; detecting nozzlemarks and said at least one nozzle locator in each said nozzle patternrows of the respective nozzle color patterns in said portion of saidnozzle pattern; for each nozzle pattern row, determining a grid for aselected group of consecutive nozzle marks including expected nozzlemark locations and actual nozzle mark locations; associating eachexpected nozzle mark location with a respective detected nozzle mark anddetermining the distance between the expected nozzle mark location andthe actual nozzle mark location of the respective nozzle mark;identifying and classifying nozzles according to the expected nozzlemark location on said grid, the actual nozzle mark location on saidgrid, the at least one nozzle locator and k;
 3. The method according toclaim 2, wherein a nozzle is classified as one of: intact; missing;deviated; inconsistent; and redundant.
 4. The method according to claim3, wherein nozzle is classified as intact when a nozzle mark isidentified in an expected nozzle mark location within a determinedtolerance, wherein a nozzle is classified as missing when an expectednozzle mark location is not associated with a detected nozzle mark,wherein a nozzle is classified as deviated when the distance betweensaid actual nozzle mark location and said expected nozzle mark locationassociated with the detected nozzle mark is above an x-deviationthreshold, wherein a nozzle is classified as inconsistent when therespective detected nozzle mark thereof exhibits a strength score belowa determined strength score threshold, wherein a nozzle is classified asredundant when a nozzle mark is detected between two expected nozzlemark locations with detected nozzle marks associated therewith.
 5. Themethod according to claim 4, wherein, said strength score threshold isdetermined according to the statistics of the strength scores of theselected group of consecutive nozzle marks.
 6. The method according toclaim 2, wherein each nozzle locator exhibits a pre-defined shapeprinted by a predetermined respective set of nozzles.
 7. The methodaccording to claim 1, wherein identifying said at least onemalfunctioning nozzle and classifying said malfunction according to saidat least a portion of said uniformity pattern includes the proceduresof: printing said at least said portion of said uniformity pattern on asubstrate, said uniformity pattern includes a plurality of coloruniformity patterns respective of each color being printed, each one ofsaid color uniformity patterns includes a plurality of color uniformityrows, each color uniformity row in each color uniformity pattern isassociated with a respective different planned ink density level.acquiring an image of said portion of said uniformity pattern;identifying color uniformity rows in the acquired image; determining therelative density of each bin in each color uniformity row, said binbeing defined as at least one pixel in the acquired image; determiningrequired correction for each bin which exhibits a non-uniform relativedensity.
 8. The method according to claim 1, wherein identifying saidmalfunctioning nozzle according to said at least one acquired image ofsaid printed design includes the sub-procedures of: turning off each ofat least one suspected nozzle; for each of the at least one turned offsuspected nozzle printing another repetition of said design; acquiringan image of said other repetition, wherein when a change occurred in theacquired image of said other repetition, relative to said acquiredimage, then the nozzle that was turned off is not the malfunctioningnozzle, wherein when no change occurred in the acquired image of saidother repetition, then the nozzle that was turned off is determined tobe a malfunctioning nozzle.
 9. The method according to claim 1, whereinidentifying said malfunctioning nozzle according to said at least oneacquired image of said printed design includes the sub-procedures of:compensating for at least one suspected nozzle; for each of the at leastone compensated suspected nozzle printing another repetition of saiddesign; acquiring an image of said other repetition, wherein, when noartifacts appear in said other acquired image relative to said acquiredimage, then the malfunctioning nozzle is identified and compensated for.10. A method of identifying at least one malfunctioning nozzle in adigital printing press, said digital printing press includes a pluralityof nozzles, the method comprising the procedures of: printing at leastone of at least a portion of a nozzle pattern and at least a portion ofa uniformity pattern; acquiring at least one image of said at least oneof at least a portion of said nozzle pattern and at least a portion ofsaid uniformity pattern; identifying said malfunctioning nozzle andclassifying said at least one malfunction nozzle according to said atleast one acquired image of said at least one of said at least a portionof said nozzle pattern and said at least a portion of said uniformitypattern.
 11. The method according to claim 10, wherein said methodincludes the preliminary procedure of; printing a design on a substrate;acquiring at least one image of the printed design; identifying at leastone artifact in the acquired image;
 12. The method according to claim11, wherein said identifying said malfunctioning nozzle and classifyingsaid at least one malfunction nozzle is performed according to said atleast one image of said printed design.
 13. The method according toclaim 12, wherein identifying said malfunctioning nozzle according tosaid at least one acquired image of said printed design includes thesub-procedures of: turning off at least one suspected nozzle; for eachof the at least one turned off suspected nozzle printing anotherrepetition of said design; acquiring an image of said other repetition,wherein when a change occurred in the acquired image of said otherrepetition, relative to said acquire image, then the nozzle that wasturned off is not the malfunctioning nozzle, wherein when no changeoccurred in the acquired image of said other repetition, then the nozzlethat was turned off is determined to be a malfunctioning nozzle.
 14. Themethod according to claim 12, wherein identifying said malfunctioningnozzle according to said at least one acquired image of said printeddesign includes the sub-procedures of: compensating at least onesuspected nozzle; for each of the at least one compensated suspectednozzle printing another repetition of said design; acquiring an image ofsaid other repetition, wherein, when no artifacts appear in said otheracquired image relative to said acquired image, then the malfunctioningnozzle is identified and compensated for.
 15. The method according toclaim 10, wherein identifying said at least one malfunctioning nozzleand classifying said malfunction according said at least a portion ofsaid nozzle pattern includes the procedures of: printing at least saidportion of said nozzle pattern, said nozzle pattern includes a nozzlecolor pattern for each of at least one printed color, each nozzle colorpattern includes a plurality of nozzle pattern rows, each nozzle patternrow includes a plurality of nozzle marks representing samples of 1 in Knozzles, each row is shifted by an offset of 1 nozzle, each nozzlepattern row further including at least one nozzle locator; acquiring animage of the printed portion of said nozzle pattern; detecting nozzlemarks and said at least one nozzle locator in each said nozzle patternrows of the respective nozzle color patterns in said portion of saidnozzle pattern; for each nozzle pattern row, determining a grid for aselected group of consecutive nozzle marks including expected nozzlemark locations and actual nozzle mark locations; associating eachexpected nozzle mark location with a respective detected nozzle mark anddetermining the distance between the expected nozzle mark location andthe actual nozzle mark location of the respective nozzle mark;identifying and classifying nozzles according to the expected nozzlemark location on said grid, the actual nozzle mark location on saidgrid, the at least one nozzle locator and k;
 16. The method according toclaim 15, wherein a nozzle is classified as one of: intact; missing;deviated; inconsistent; and redundant.
 17. The method according to claim16, wherein nozzle is classified as intact when a nozzle mark isidentified in an expected nozzle mark location within a determinedtolerance, wherein a nozzle is classified as missing when an expectednozzle mark location is not associated with a detected nozzle mark,wherein a nozzle is classified as deviated when the distance betweensaid actual nozzle mark location and said expected nozzle mark locationassociated with the detected nozzle mark is above an x-deviationthreshold, wherein a nozzle is classified as inconsistent when therespective detected nozzle mark thereof exhibits a strength score belowa determined strength score threshold, wherein a nozzle is classified asredundant when a nozzle mark is detected between two expected nozzlemark locations with detected nozzle marks associated therewith.
 18. Themethod according to claim 17, wherein, said strength score threshold isdetermined according to the statistics of the strength scores of theselected group of consecutive nozzle marks.
 19. The method according toclaim 15, wherein each nozzle locator exhibits a pre-defined shapeprinted by a predetermined respective set of nozzles.
 20. The methodaccording to claim 10, wherein identifying said at least onemalfunctioning nozzle and classifying said malfunction according to saidat least a portion of said uniformity pattern includes the proceduresof: printing said at least said portion of said uniformity pattern on asubstrate, said uniformity pattern includes a plurality of coloruniformity patterns respective of each color being printed, each one ofsaid color uniformity patterns includes a plurality of color uniformityrows, each color uniformity row in each color uniformity pattern isassociated with a respective different planned ink density level.acquiring an image of said portion of said uniformity pattern;identifying color uniformity rows in the acquired image; determining therelative density of each bin in each color uniformity row, said binbeing defined as at least one pixel in the acquired image; determiningrequired correction for each bin which exhibits a non-uniform relativedensity.